| Autor: |
Olivier Boulant, Nicolas Vayatis, Anne Flore Baron, Ivan Panico |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Image Processing On Line. 11:105-119 |
| ISSN: |
2105-1232 |
| Popis: |
The objective of this work is to provide a sophisticated but accessible compartmental epidemic model. Our algorithm is highly inspired from the compartmental model developed by Sofonea and al. in 2020. This model has been used as a reference for several working groups in France during the Covid-19 crisis. Each individual is allocated to a compartment according to her age, her current state with respect to the disease, as well as the length of time she has been in that state. The model then reproduces the mechanisms of transition from one state to another: mathematically, this translates into a system of recurrence relations. It captures how much individuals interact with one another through a parameter that estimates compliance with hygiene measures and lifestyle habits. The present work aims to make the model implementation fully transparent as well as the corresponding code available and give control to users so that they are able to test the model in total transparency. Focus has been put on reproducibility and explanation of the various parameters. The hard-coded parameters correspond to the data for the Covid-19 epidemic in France. © 2021 IPOL & the authors. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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